Dendroclimatological Analysis of Fir (A. borisii-regis) in Greece in the frame of Climate Change Investigation

Tree Resilience Indices of Norway Spruce Provenances Tested in Long-Term Common Garden Experiments in the Romanian Carpathians

Provenance trials provide a valuable opportunity to evaluate the impact of extreme events on growth and wood properties. In this study, we have evaluated 81 Norway spruce provenances, tested in three provenance trials established in the Romanian Carpathians in 1972. The response to drought of the Norway spruce provenances has been examined using the following tree resilience indices: resistance, recovery, resilience, and relative resilience. The relationship between climate and growth, the correlations between wood traits, and the coordinates of the origin and tree resilience indices were also analysed. In each provenance trial, there were significant differences between provenances and years regarding wood widths and latewood percentage (LWP). Regarding drought extreme events, the years when they occurred in all three provenance trials were 2000 and 2003. Significant differences between provenances for at least one tree resilience index have been found in all provenance trials, for the year 2000. By using subperiods of 25 years, changes in the relationship between climate and growth have been observed. Several provenances with high radial growth and good resistance and/or recovery have been identified. Provenances that performed better in common garden experiments could be used in assisted migration, even in the proximity of the current natural range.

Reconstruction of Hydrometeorological Data Using Dendrochronology and Machine Learning Approaches to Bias-Correct Climate Models in Northern Tien Shan, Kyrgyzstan

Tree-ring-width chronologies for 33 samples of Picea abies (L.) Karst. were developed, and a relationship between tree growth and hydrometeorological features was established and analyzed. Precipitation, temperature, and discharge records were extrapolated to understand past climate trends to evaluate the accuracy of global climate models (GCMs). Using Machine Learning (ML) approaches, hydrometeorological records were reconstructed/extrapolated back to 1886. An increase in the mean annual temperature (Tmeana) increased the mean annual discharge (Dmeana) via glacier melting; however, no temporal trends in annual precipitation were detected. For these reconstructed climate data, root-mean-square error (RMSE), Taylor diagrams, and Kling–Gupta efficiency (KGE) were used to evaluate and assess the robustness of GCMs. The CORDEX REMO models indicated the best performance for simulating precipitation and temperature over northern Tien Shan; these models replicated historical Tmena and Pa quite well (KGE = 0.24 and KGE = 0.24, respectively). Moreover, the multi-model ensembles with selected GCMs and bias correction can significantly increase the performance of climate models, especially for mountains region where small-scale orographic effects abound.